Authors : Edwin Rupi; Precious Mdlongwa; Peter Chimwanda; Philimon Nyamugure

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/534scz38

Scribd : https://tinyurl.com/3xwnnyws

DOI : https://doi.org/10.38124/ijisrt/26feb711

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Sugarcane production in Zimbabwe’s Eastern Low-veld is characterized by pronounced spatial and seasonal environmental variability that complicates agronomic decision-making and yield optimization. This study developed and evaluated a response surface modelling (RSM) framework integrated with environmental indicators to quantify how agronomic inputs interact with climatic variability in determining sugarcane yield. Using multi-site, multi-season field data from irrigated sugarcane systems in Chiredzi District, baseline RSMs were first fitted using fertilizer rate, irrigation amount, and plant density. The modelling framework was subsequently extended using mixed-effects regression to incorporate temperature, precipitation and humidity while accounting for spatial and temporal heterogeneity. The integrated models explained a high proportion of yield variability (R² up to 0.865) and exhibited strong predictive accuracy (RMSE = 1.99; MAE = 1.31). Significant curvature and interaction effects confirmed that yield responses are highly context-dependent, with optimal agronomic input combinations varying across environmental scenarios. Scenario-based optimization demonstrated that maximum yield potential is substantially higher under favourable thermal and moisture conditions, although optimal management shifts toward water-intensive, lower-density systems. The results highlight the importance of adaptive, environment-conditioned agronomic strategies and provide a robust modelling framework for climate-responsive sugarcane management in Zimbabwe’s Low-veld.

Keywords : Sugarcane Yield, Response Surface Methodology, Environmental Variability, Mixed-Effects Models, Reference Evapotranspiration.

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